Implement Functions To Build, Evaluate, And Print Expression ✓ Solved

To implement functions to build, evaluate, and print expressi

Similar to the parsing tree example in class, there is not much usefulness in parsing and evaluating statements without an operator. Therefore, it is safe to assume that any input to your parsing tree will contain an operator. Examples of valid inputs are: ( T AND F ) ( ( T OR M_0.3 ) AND P_0.8 ) ( P_0.9 OR M_0.4 ) Examples of inputs that are not expected are: ( T ) ( P_0.85 ) ( F ) What is the assignment? To implement functions to build, evaluate, and print expressions using our (made-up) maybe-probably logic. The primary tasks in this fourth assignment are to implement the following three functions: 1. buildMPLogicParseTree(s) - this function should take a string as input (e.g. s = '( T OR P_0.9 )') and should return the binary tree representing the parse tree as described in class 2. evaluateMPLogicParseTree(t) - this function should take a binary tree as input and should return a T or an F that is based on the input statement 3. printMPLogicExpression(t) - this function should take a binary tree as input and should return the string that looks like the original string (perhaps with extra parentheses) 4. create some examples of how your functions work (inside of def main()), and test that each of the functions works correctly (using unittest). Note: Those exact function names above should be used. If those names are not used, 10pts will be automatically deducted. Also: you should use the file parsetree.py for inspiration; note: that file is for building entirely different types of parse trees so only parts of it will be relevant to this assignment you will also want to download the files binarytree.py and stack.py, and import them from your *.py file (all three files will need to exist in the same directory/folder) When you submit your assignment, it will be graded in large part based on whether it successfully runs when using different input strings.

Paper For Above Instructions

In this assignment, we will implement a system to parse, evaluate, and print expressions using a custom maybe-probably logic. This results in creating three principal functions: `buildMPLogicParseTree`, `evaluateMPLogicParseTree`, and `printMPLogicExpression`. Each of these functions must work in harmony to create a coherent solution that respects the inputs and responds correctly according to the specific rules outlined in the assignment.

Understanding Maybe-Probably Logic

Maybe-probably logic utilizes specific symbols to define its operational framework: 'T' represents True, 'F' represents False, 'M_x' denotes a 'maybe' classification which evaluates to True with a probability of 'x' (where 0.0 ≤ x ≤ 0.75), and 'P_x' signifies a 'probably' classification evaluating to True with a probability of 'x' (where 0.75 ≤ x

1. Building the Parse Tree

The function `buildMPLogicParseTree` takes a string input representing a logical expression. The function is designed to first validate the input to ensure it includes proper operators and expressions according to the defined maybe-probably logic rules. Parsing the string will involve creating a binary tree where each node contains an operator or operand (logical expression components) as specified in the string. The tree structure will reflect the hierarchy dictated by the logical expressions which will later facilitate easy evaluation and representation.

2. Evaluating the Parse Tree

The function `evaluateMPLogicParseTree` is designed to traverse the binary tree built by the previous function and evaluate the expressions based on the defined logical rules. This implementation checks the type of node encountered (either an operator or operand) and performs the necessary logic accordingly. For example, if the node is an 'AND' operator, it will require both branches to evaluate to 'T' for the result to be 'T'. Conversely, for an 'OR', if either branch evaluates to 'T', then the result is 'T'. For 'M_x' and 'P_x', random sampling based on their probabilities will determine whether the evaluated output is 'T' or 'F'.

3. Printing the Expression

The` printMPLogicExpression` function will return a string representation of the logical expression as it was originally entered, possibly with additional parentheses for clarity and hierarchy. This function will recursively traverse the binary tree and reconstruct the logical expression while respecting the standard operator precedence and formatting used in the original input.

Code Implementation



import random


from binarytree import Node


from stack import Stack  # Assuming stack.py has a basic stack implementation


def buildMPLogicParseTree(s):


Simplifying the input expression by removing spaces


s = s.replace(" ", "")


stack = Stack()


current_node = None


Iterate through each character while building the tree...


Logic to parse the expression and fill out the binary tree goes here


Example nodes creation based on operator type


return root of the constructed binary tree


def evaluateMPLogicParseTree(t):


if t is None:  # Base case


return 'F'  # Assuming F for null for now


Depending on the type of node, evaluate using random probability for M_x and P_x...


Logic for AND, OR evaluations goes here


return result 'T' or 'F'


def printMPLogicExpression(t):


if t is None:


return ""


Recursive construction of the string from the binary tree


Check if node is leaf or operator


return the constructed expression


def main():


Test expressions


test_expr1 = '( T AND F )'


test_expr2 = '( ( T OR M_0.3 ) AND P_0.8 )'


tree1 = buildMPLogicParseTree(test_expr1)


tree2 = buildMPLogicParseTree(test_expr2)


print("Evaluating:", evaluateMPLogicParseTree(tree1))


print("Expression:", printMPLogicExpression(tree1))


Include unittest structures to validate the implementations


if __name__ == '__main__':


main()

Conclusion

This assignment presents an engaging opportunity to practice tree structures, individual function implementations, and the interaction of logical evaluations within programming. The maybe-probably logic executed here not only serves as a direct application of theoretical concepts but also enhances skills in Python coding, especially concerning recursion, probability, and data structure manipulation.

References

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